In Bed with Autobiography: Unravelling the Fabric of Gender and Genre in Women’s Bed(room) Confessions

This thesis consists of a confessional narrative, What My Mother Doesn’t Know, and an accompanying exegesis, And Why I Should (Maybe) Tell Her. The creative piece employs the confessional mode as a subversive device in three separate narratives, each of which situates the bed as a site of resistance. The exegesis investigates how this self-disclosure in a domestic space flouts the governing rules of self-representation, specifically: telling the truth, respecting privacy and displaying normalcy. The female confession, I argue, creates an alternative space in women’s autobiography where notions of truth-telling can be undermined, the political dimensions of personal experience can be uncovered and the discourse of normality can be negotiated. In particular, women’s confessions told in, on or about the bed, dismantle the genre’s illusion of self and confirm the representative aspects of women’s experience. Framed within these parameters of power and powerlessness, the exegesis includes textual analyses of Charlotte Perkins Gilman’s The Yellow Wallpaper (1892), Tracey Emin’s My Bed (1999) and Lauren Slater’s Lying (2000), each of which exposes in a bedroom space, the author’s most obscure, intimate and traumatic experiences. Situated firmly within and against the genre’s traditional masculine domain, the exegesis also includes mediations on the creative work that validate the bed as my fabric for confession.

Temporal-based support vector machines for facial expression recognition

When classifying a signal, ideally we want our classifier to trigger a large response when it encounters a positive example and have little to no response for all other examples. Unfortunately in practice this does not occur with responses fluctuating, often causing false alarms. There exists a myriad of reasons why this is the case, most notably not incorporating the dynamics of the signal into the classification. In facial expression recognition, this has been highlighted as one major research question. In this paper we present a novel technique which incorporates the dynamics of the signal which can produce a strong response when the peak expression is found and essentially suppresses all other responses as much as possible. We conducted preliminary experiments on the extended Cohn-Kanade (CK+) database which shows its benefits. The ability to automatically and accurately recognize facial expressions of drivers is highly relevant to the automobile. For example, the early recognition of “surprise” could indicate that an accident is about to occur; and various safeguards could immediately be deployed to avoid or minimize injury and damage. In this paper, we conducted initial experiments on the extended Cohn-Kanade (CK+) database which shows its benefits.

Robust image hashing using higher order spectral features

Robust image hashing seeks to transform a given input image into a shorter hashed version using a key-dependent non-invertible transform. These image hashes can be used for watermarking, image integrity authentication or image indexing for fast retrieval. This paper introduces a new method of generating image hashes based on extracting Higher Order Spectral features from the Radon projection of an input image. The feature extraction process is non-invertible, non-linear and different hashes can be produced from the same image through the use of random permutations of the input. We show that the transform is robust to typical image transformations such as JPEG compression, noise, scaling, rotation, smoothing and cropping. We evaluate our system using a verification-style framework based on calculating false match, false non-match likelihoods using the publicly available Uncompressed Colour Image database (UCID) of 1320 images. We also compare our results to Swaminathan’s Fourier-Mellin based hashing method with at least 1% EER improvement under noise, scaling and sharpening.

The stewardship paradigm : an enquiry into the ethical obligation associated with being in control of resorces

The resource allocation and utilization discourse is dominated by debates about rights particularly individual property rights and ownership. This is due largely to the philosophic foundations provided by Hobbes and Locke and adopted by Bentham. In our community, though, resources come not merely with rights embedded but also obligations. The relevant laws and equitable principles which give shape to our shared rights and obligations with respect to resources take cognizance not merely of the title to the resource (the proprietary right) but the particular context in which the right is exercised. Moral philosophy regarding resource utilisation has from ancient times taken cognizance of obligations but with ascendance of modernity, the agenda of moral philosophy regarding resources, has been dominated, at least since John Locke, by a preoccupation with property rights; the ethical obligations associated with resource management have been largely ignored. The particular social context has also been ignored. Exploring this applied ethical terrain regarding resource utilisation, this thesis: (1) Revisits the justifications for modem property rights (and in that the exclusion of obligations); (2) Identifies major deficiencies in these justifications and reasons for this; (3) Traces the concept of stewardship as understood in classical Greek writing and in the New Testament, and considers its application in the Patristic period and by Medieval and reformist writers, before turning to investigate its influence on legal and equitable concepts through to the current day; 4) Discusses the nature of the stewardship obligation,maps it and offers a schematic for applying the Stewardship Paradigm to problems arising in daily life; and, (5) Discusses the way in which the Stewardship Paradigm may be applied by, and assists in resolving issues arising from within four dominant philosophic world views: (a) Rawls' social contract theory; (b) Utilitarianism as discussed by Peter Singer; (c) Christianity with particular focus on the theology of Douglas Hall; (d) Feminism particularly as expressed in the ethics of care of Carol Gilligan; and, offers some more general comments about stewardship in the context of an ethically plural community.

Acoustic keyword spotting in speech with applications to data mining

Keyword Spotting is the task of detecting keywords of interest within continu- ous speech. The applications of this technology range from call centre dialogue systems to covert speech surveillance devices. Keyword spotting is particularly well suited to data mining tasks such as real-time keyword monitoring and unre- stricted vocabulary audio document indexing. However, to date, many keyword spotting approaches have su®ered from poor detection rates, high false alarm rates, or slow execution times, thus reducing their commercial viability. This work investigates the application of keyword spotting to data mining tasks. The thesis makes a number of major contributions to the ¯eld of keyword spotting. The ¯rst major contribution is the development of a novel keyword veri¯cation method named Cohort Word Veri¯cation. This method combines high level lin- guistic information with cohort-based veri¯cation techniques to obtain dramatic improvements in veri¯cation performance, in particular for the problematic short duration target word class. The second major contribution is the development of a novel audio document indexing technique named Dynamic Match Lattice Spotting. This technique aug- ments lattice-based audio indexing principles with dynamic sequence matching techniques to provide robustness to erroneous lattice realisations. The resulting algorithm obtains signi¯cant improvement in detection rate over lattice-based audio document indexing while still maintaining extremely fast search speeds. The third major contribution is the study of multiple veri¯er fusion for the task of keyword veri¯cation. The reported experiments demonstrate that substantial improvements in veri¯cation performance can be obtained through the fusion of multiple keyword veri¯ers. The research focuses on combinations of speech background model based veri¯ers and cohort word veri¯ers. The ¯nal major contribution is a comprehensive study of the e®ects of limited training data for keyword spotting. This study is performed with consideration as to how these e®ects impact the immediate development and deployment of speech technologies for non-English languages.

Real-time performance modelling of a sustained attention to response task

Vigilance declines when exposed to highly predictable and uneventful tasks. Monotonous tasks provide little cognitive and motor stimulation and contribute to human errors. This paper aims to model and detect vigilance decline in real time through participant’s reaction times during a monotonous task. A lab-based experiment adapting the Sustained Attention to Response Task (SART) is conducted to quantify the effect of monotony on overall performance. Then relevant parameters are used to build a model detecting hypovigilance throughout the experiment. The accuracy of different mathematical models are compared to detect in real-time – minute by minute - the lapses in vigilance during the task. We show that monotonous tasks can lead to an average decline in performance of 45%. Furthermore, vigilance modelling enables to detect vigilance decline through reaction times with an accuracy of 72% and a 29% false alarm rate. Bayesian models are identified as a better model to detect lapses in vigilance as compared to Neural Networks and Generalised Linear Mixed Models. This modelling could be used as a framework to detect vigilance decline of any human performing monotonous tasks.

Rice Tungro Bacilliform

Many well-known specialists have contributed to this book which presents for the first time an in-depth look at the viruses, their satellites and the retrotransposons infecting (or occuring in) one plant family: the Poaceae (Gramineae). After molecular and biological descriptions of the viruses to species level, virus diseases are presented by crop: barley, maize, rice, rye, sorghum, sugarcane, triticales, wheats, forage, ornamental and lawn. A detailed index of the viruses and taxonomic lists will help readers in the search for information.

An improved modal strain energy method for damage assessment

The modal strain energy method, which depends on the vibration characteristics of the structure, has been reasonably successful in identifying and localising damage in the structure. However, existing strain energy methods require the first few modes to be measured to provide meaningful damage detection. Use of individual modes with existing strain energy methods may indicate false alarms or may not detect the damage at or near the nodal points. This paper proposes a new modal strain energy based damage index which can detect and localize the damage using any one of the modes measured and illustrates its application for beam structures. It becomes evident that the proposed strain energy based damage index also has potential for damage quantification.

Focus-score weighted super-resolution for uncooperative iris recognition at a distance and on the move

Uncooperative iris identification systems at a distance and on the move often suffer from poor resolution and poor focus of the captured iris images. The lack of pixel resolution and well-focused images significantly degrades the iris recognition performance. This paper proposes a new approach to incorporate the focus score into a reconstruction-based super-resolution process to generate a high resolution iris image from a low resolution and focus inconsistent video sequence of an eye. A reconstruction-based technique, which can incorporate middle and high frequency components from multiple low resolution frames into one desired super-resolved frame without introducing false high frequency components, is used. A new focus assessment approach is proposed for uncooperative iris at a distance and on the move to improve performance for variations in lighting, size and occlusion. A novel fusion scheme is then proposed to incorporate the proposed focus score into the super-resolution process. The experiments conducted on the The Multiple Biometric Grand Challenge portal database shows that our proposed approach achieves an EER of 2.1%, outperforming the existing state-of-the-art averaging signal-level fusion approach by 19.2% and the robust mean super-resolution approach by 8.7%.

Remembering beliefs

Optimal decision-making requires us to accurately pinpoint the basis of our thoughts, e.g. whether they originate from our memory or our imagination. This paper argues that the phenomenal qualities of our subjective experience provide permissible evidence to revise beliefs, particularly as it pertains to memory. I look to the source monitoring literature to reconcile circumstances where mnemic beliefs and mnemic qualia conflict. By separating the experience of remembering from biological facts of memory, unusual cases make sense, such as memory qualia without memory (e.g. déjà vu, false memories) or a failure to have memory qualia with memory (e.g. functional amnesia, unintentional plagiarism). I argue that a pragmatic, probabilistic approach to belief revision is a way to rationally incorporate information from conscious experience, whilst acknowledging its inherent difficulties as an epistemic source. I conclude with a Bayesian defense of source monitoring based on C.I. Lewis’ coherence argument for memorial knowledge.

New criteria for evaluating methods of identifying hot spots

Identification of hot spots, also known as the sites with promise, black spots, accident-prone locations, or priority investigation locations, is an important and routine activity for improving the overall safety of roadway networks. Extensive literature focuses on methods for hot spot identification (HSID). A subset of this considerable literature is dedicated to conducting performance assessments of various HSID methods. A central issue in comparing HSID methods is the development and selection of quantitative and qualitative performance measures or criteria. The authors contend that currently employed HSID assessment criteria—namely false positives and false negatives—are necessary but not sufficient, and additional criteria are needed to exploit the ordinal nature of site ranking data. With the intent to equip road safety professionals and researchers with more useful tools to compare the performances of various HSID methods and to improve the level of HSID assessments, this paper proposes four quantitative HSID evaluation tests that are, to the authors’ knowledge, new and unique. These tests evaluate different aspects of HSID method performance, including reliability of results, ranking consistency, and false identification consistency and reliability. It is intended that road safety professionals apply these different evaluation tests in addition to existing tests to compare the performances of various HSID methods, and then select the most appropriate HSID method to screen road networks to identify sites that require further analysis. This work demonstrates four new criteria using 3 years of Arizona road section accident data and four commonly applied HSID methods [accident frequency ranking, accident rate ranking, accident reduction potential, and empirical Bayes (EB)]. The EB HSID method reveals itself as the superior method in most of the evaluation tests. In contrast, identifying hot spots using accident rate rankings performs the least well among the tests. The accident frequency and accident reduction potential methods perform similarly, with slight differences explained. The authors believe that the four new evaluation tests offer insight into HSID performance heretofore unavailable to analysts and researchers.

Experimental evaluation of hotspot identification methods

Identifying crash “hotspots”, “blackspots”, “sites with promise”, or “high risk” locations is standard practice in departments of transportation throughout the US. The literature is replete with the development and discussion of statistical methods for hotspot identification (HSID). Theoretical derivations and empirical studies have been used to weigh the benefits of various HSID methods; however, a small number of studies have used controlled experiments to systematically assess various methods. Using experimentally derived simulated data—which are argued to be superior to empirical data, three hot spot identification methods observed in practice are evaluated: simple ranking, confidence interval, and Empirical Bayes. Using simulated data, sites with promise are known a priori, in contrast to empirical data where high risk sites are not known for certain. To conduct the evaluation, properties of observed crash data are used to generate simulated crash frequency distributions at hypothetical sites. A variety of factors is manipulated to simulate a host of ‘real world’ conditions. Various levels of confidence are explored, and false positives (identifying a safe site as high risk) and false negatives (identifying a high risk site as safe) are compared across methods. Finally, the effects of crash history duration in the three HSID approaches are assessed. The results illustrate that the Empirical Bayes technique significantly outperforms ranking and confidence interval techniques (with certain caveats). As found by others, false positives and negatives are inversely related. Three years of crash history appears, in general, to provide an appropriate crash history duration.

Secure tracking for critical applications : communications, GPS and future Galileo services

Tracking/remote monitoring systems using GNSS are a proven method to enhance the safety and security of personnel and vehicles carrying precious or hazardous cargo. While GNSS tracking appears to mitigate some of these threats, if not adequately secured, it can be a double-edged sword allowing adversaries to obtain sensitive shipment and vehicle position data to better coordinate their attacks, and to provide a false sense of security to monitoring centers. Tracking systems must be designed with the ability to perform route-compliance and thwart attacks ranging from low-level attacks such as the cutting of antenna cables to medium and high-level attacks involving radio jamming and signal / data-level simulation, especially where the goods transported have a potentially high value to terrorists. This paper discusses the use of GNSS in critical tracking applications, addressing the mitigation of GNSS security issues, augmentation systems and communication systems in order to provide highly robust and survivable tracking systems.

Continuous biometric authentication : can it be more practical?

Continuous biometric authentication schemes (CBAS) are built around the biometrics supplied by user behavioural characteristics and continuously check the identity of the user throughout the session. The current literature for CBAS primarily focuses on the accuracy of the system in order to reduce false alarms. However, these attempts do not consider various issues that might affect practicality in real world applications and continuous authentication scenarios. One of the main issues is that the presented CBAS are based on several samples of training data either of both intruder and valid users or only the valid users' profile. This means that historical profiles for either the legitimate users or possible attackers should be available or collected before prediction time. However, in some cases it is impractical to gain the biometric data of the user in advance (before detection time). Another issue is the variability of the behaviour of the user between the registered profile obtained during enrollment, and the profile from the testing phase. The aim of this paper is to identify the limitations in current CBAS in order to make them more practical for real world applications. Also, the paper discusses a new application for CBAS not requiring any training data either from intruders or from valid users.

Development of a novel rolling-circle amplification technique to detect Banana streak virus which also discriminates between integrated and episomal virus sequences

Bananas are hosts to a large number of banana streak virus (BSV) species. However, diagnostic methods for BSV are inadequate because of the considerable genetic and serological diversity amongst BSV isolates and the presence of integrated BSV sequences in some banana cultivars which leads to false positives. In this study, a sequence non-specific, rolling-circle amplification (RCA) technique was developed and shown to overcome these limitations for the detection and subsequent characterisation of BSV isolates infecting banana. This technique was shown to discriminate between integrated and episomal BSV DNA, specifically detecting the latter in several banana cultivars known to contain episomal and/or integrated sequences of Banana streak Mysore virus (BSMyV), Banana streak OL virus (BSOLV) and Banana streak GF virus (BSGFV). Using RCA, the presence of BSMyV and BSOLV was confirmed in Australia, while BSOLV, BSGFV, Banana streak Uganda I virus (BSUgIV), Banana streak Uganda L virus (BSUgLV) and Banana streak Uganda M virus (BSUgMV) were detected in Uganda. This is the first confirmed report of episomally-derived BSUglV, BSUgLV and BSUgMV in Uganda. As well as its ability to detect BSV, RCA was shown to detect two other pararetroviruses, Sugarcane bacilliform virus in sugarcane and Cauliflower mosaic virus in turnip.